Aggregate Feature Records for Real-Time ML Inference
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Solution Overview
Problem
Existing machine learning systems face challenges in efficiently generating and utilizing historical data features for improved predictions, as they are often expensive to store and require significant processing cycles, limiting their ability to provide real-time and personalized recommendations.
Innovation Solution
A flexible infrastructure is developed to define, calculate, and use historical data features, allowing for on-the-fly addition of new aggregates and metrics, with a schema that includes aggregate definitions, an aggregation engine, and a query service to generate and index aggregate feature records, enabling real-time processing and low-latency responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If historical data is stored and processed to generate features for machine learning models, then prediction accuracy is improved, but storage cost and processing cycle time increase
Solution Approach 1:
The patent extracts only the necessary aggregate metrics (count, average, sum) from the full historical data, rather than storing and processing all raw data. This extraction approach retains the essential information needed for accurate predictions while significantly reducing storage requirements and processing overhead.
Solution Approach 2:
The system pre-calculates and stores aggregate metrics in a structured format before they are needed for prediction. By performing this aggregation action in advance and organizing it in a queryable structure, the system avoids expensive real-time aggregation operations while maintaining access to historical patterns for accurate predictions.
2Measurement precision
If historical metrics are calculated to improve model predictions, then prediction accuracy is improved, but processing cycle time increases
Solution Approach 1:
The patent performs aggregate metric calculations in advance during data ingestion or batch processing, storing the results in a pre-computed format. This preliminary action eliminates the need for expensive real-time aggregation operations when making predictions, significantly reducing processing cycle time while maintaining prediction accuracy.
Solution Approach 2:
The system segments the data processing into distinct phases: initial data aggregation, metric calculation, and prediction execution. By separating these operations and storing intermediate aggregate results, the patent enables fast prediction responses without the computational burden of recalculating all historical metrics during each prediction cycle.
3Measurement precision
If aggregate features are added to improve model performance, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal aggregation framework that can handle multiple different aggregate operations (count, average, sum, min, max) and multiple time windows through a single consistent interface and data structure. This multi-functional design allows the system to add new aggregate features for improved prediction accuracy without increasing operational complexity, as the same infrastructure handles all aggregation types.
Data Source
AI summary
An example system includes a memory store of aggregate definitions. Each aggregate definition specifies a key value, an output store, a feature, a half-life value, and an aggregate operation metric to apply to a cross of the feature and the half-life value to generate aggregate metrics. The system also includes an aggregation engine that generates aggregate feature records from the input source based on the aggregate definitions and stores the aggregate feature records in the output store. An aggregate feature record includes an aggregate of the metric for the feature decayed over time using the half-life. The system also includes a query service that identifies, using the aggregate definitions, responsive aggregate feature records that satisfy parameters of a received request, applies the half-life to the responsive feature records, and provides the responsive feature records to a requester, the requester using the responsive feature records as input for a neural network.